This paper analyzes the roles of problem solving and learning in Organizational-learning oriented Classifier System (OCS) from the viewpoint of organizational learning in organization and management sciences, and validates the effectiveness of the roles through the experiments of large scale problem for Printed Circuit Boards (PCBs) re-design in the Computer Aided Design (CAD). OCS is a novel multiagent-based architecture, and is composed of the following four mechanisms: (1) reinforcement learning, (2) rule generation, (3) rule exchange, and (4) organizational knowledge utilization. In this paper, we discuss that the four mechanisms in OCS work respectively as an individual performance/concept learning and an organizational performance/concept learning in organization and management sciences. Through the intensive experiments on the re-design problems of real scale PCBs, the results suggested that four learning mechanisms in individual/organizational levels contribute to finding not only feasible part placements in fewer iterations but also the shorter total wiring length than the one by human experts.
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